Interval Estimation for Youden Index of a Continuous Diagnostic Test with Verification Biased Data

Music City Center 

In clinical practice, missing disease status verification is common and can bias estimators of diagnostic test accuracy. In this paper, we propose verification bias-corrected interval estimation methods for Youden index of a continuous test under the missing-at-random (MAR) assumption. Based on four estimators (FI, MSI, IPW, and SPE) introduced by Alonzo and Pepe for handling verification bias, we develop multiple confidence intervals for the Youden index by applying bootstrap resampling and the method of variance estimates recovery (MOVER). Through extensive simulation and real data studies, we find SPE estimator performs better when paired with bootstrap method. Notably, bootstrap-SPE intervals show appealing doubly robustness to the model misspecification and perform adequately across almost all scenarios considered. In contrast, FI and MSI estimators perform better when paired with MOVER method. When the disease model is correctly specified, MOVER-FI intervals achieve optimal coverage probability. We also find that when the verification proportion is low, bootstrap methods provide more accurate estimates while MOVER methods offer higher precision.

Youden index

Receiver operating characteristic (ROC) curve

Verification bias

Missing at random

Diagnostic test

Bootstrap resampling,
Method of variance estimates recovery 

Section on Medical Devices and Diagnostics